Overview

Dataset statistics

Number of variables38
Number of observations160139
Missing cells3279
Missing cells (%)0.1%
Duplicate rows4382
Duplicate rows (%)2.7%
Total size in memory46.4 MiB
Average record size in memory304.0 B

Variable types

Numeric6
Categorical32

Alerts

Dataset has 4382 (2.7%) duplicate rowsDuplicates
Age is highly overall correlated with EL_primary and 7 other fieldsHigh correlation
EL_higherprofessional_university is highly overall correlated with EL_secondary_higherHigh correlation
EL_primary is highly overall correlated with Age and 1 other fieldsHigh correlation
EL_secondary_higher is highly overall correlated with EL_higherprofessional_universityHigh correlation
Ethn_dutch is highly overall correlated with Ethn_nonwestern and 1 other fieldsHigh correlation
Ethn_nonwestern is highly overall correlated with Ethn_dutchHigh correlation
Ethn_western is highly overall correlated with Ethn_dutchHigh correlation
HHC_couple is highly overall correlated with Age and 1 other fieldsHigh correlation
HHC_couple_with_children is highly overall correlated with Age and 1 other fieldsHigh correlation
Main_moti_sparetime is highly overall correlated with Main_moti_workHigh correlation
Main_moti_work is highly overall correlated with Main_moti_sparetimeHigh correlation
Moti_sparetime is highly overall correlated with Moti_workHigh correlation
Moti_work is highly overall correlated with Moti_sparetimeHigh correlation
PW_no is highly overall correlated with Age and 2 other fieldsHigh correlation
PW_yesmorethan30h is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_benefits is highly overall correlated with Age and 1 other fieldsHigh correlation
UO_none is highly overall correlated with Age and 4 other fieldsHigh correlation
UO_student/scholar is highly overall correlated with Age and 2 other fieldsHigh correlation
Part_of_sequence is highly imbalanced (52.0%)Imbalance
PW_yeslessthan12h is highly imbalanced (73.3%)Imbalance
HHC_oneperson_with_children is highly imbalanced (63.5%)Imbalance
Ethn_western is highly imbalanced (57.7%)Imbalance
Ethn_nonwestern is highly imbalanced (53.7%)Imbalance
Moti_profession is highly imbalanced (92.3%)Imbalance
Moti_pickupdropoff_person is highly imbalanced (64.5%)Imbalance
Main_moti_profession is highly imbalanced (87.1%)Imbalance
Main_moti_pickupdropoff_person is highly imbalanced (74.7%)Imbalance
Starting_postalcode has 3279 (2.0%) missing valuesMissing
Number_of_cars_in_HH has 24468 (15.3%) zerosZeros

Reproduction

Analysis started2024-07-05 11:45:16.074007
Analysis finished2024-07-05 11:46:02.990568
Duration46.92 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Starting_postalcode
Real number (ℝ)

MISSING 

Distinct3680
Distinct (%)2.3%
Missing3279
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean4570.7508
Minimum1011
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:46:03.465730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1087
Q12431
median3994
Q36641
95-th percentile9205
Maximum9999
Range8988
Interquartile range (IQR)4210

Descriptive statistics

Standard deviation2533.9896
Coefficient of variation (CV)0.55439242
Kurtosis-1.0105803
Mean4570.7508
Median Absolute Deviation (MAD)1980
Skewness0.35385892
Sum7.1696797 × 108
Variance6421103.4
MonotonicityNot monotonic
2024-07-05T13:46:03.799206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3511 567
 
0.4%
1012 483
 
0.3%
1181 411
 
0.3%
3011 404
 
0.3%
1018 398
 
0.2%
3584 383
 
0.2%
1017 366
 
0.2%
2611 327
 
0.2%
3512 312
 
0.2%
2132 310
 
0.2%
Other values (3670) 152899
95.5%
(Missing) 3279
 
2.0%
ValueCountFrequency (%)
1011 299
0.2%
1012 483
0.3%
1013 274
0.2%
1014 107
 
0.1%
1015 142
 
0.1%
1016 207
0.1%
1017 366
0.2%
1018 398
0.2%
1019 254
0.2%
1021 45
 
< 0.1%
ValueCountFrequency (%)
9999 2
 
< 0.1%
9998 3
 
< 0.1%
9997 3
 
< 0.1%
9996 3
 
< 0.1%
9995 1
 
< 0.1%
9993 1
 
< 0.1%
9991 17
< 0.1%
9989 24
< 0.1%
9988 5
 
< 0.1%
9987 2
 
< 0.1%

Gender_male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
81126 
0
79013 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160139
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 81126
50.7%
0 79013
49.3%

Length

2024-07-05T13:46:04.028066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:04.251011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 81126
50.7%
0 79013
49.3%

Most occurring characters

ValueCountFrequency (%)
1 81126
50.7%
0 79013
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160139
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 81126
50.7%
0 79013
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 160139
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 81126
50.7%
0 79013
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 81126
50.7%
0 79013
49.3%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.874178
Minimum6
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:46:04.484820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q124
median41
Q356
95-th percentile74
Maximum98
Range92
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.025926
Coefficient of variation (CV)0.48994076
Kurtosis-0.96598798
Mean40.874178
Median Absolute Deviation (MAD)16
Skewness0.13629443
Sum6545550
Variance401.0377
MonotonicityNot monotonic
2024-07-05T13:46:04.722550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 3081
 
1.9%
49 2952
 
1.8%
18 2775
 
1.7%
50 2763
 
1.7%
47 2729
 
1.7%
27 2703
 
1.7%
51 2702
 
1.7%
48 2672
 
1.7%
53 2667
 
1.7%
24 2627
 
1.6%
Other values (83) 132468
82.7%
ValueCountFrequency (%)
6 1342
0.8%
7 1497
0.9%
8 1645
1.0%
9 1976
1.2%
10 1988
1.2%
11 2118
1.3%
12 2224
1.4%
13 2208
1.4%
14 2250
1.4%
15 2429
1.5%
ValueCountFrequency (%)
98 2
 
< 0.1%
97 1
 
< 0.1%
96 11
 
< 0.1%
95 9
 
< 0.1%
94 12
 
< 0.1%
93 10
 
< 0.1%
92 20
< 0.1%
91 29
< 0.1%
90 42
< 0.1%
89 49
< 0.1%

Number_of_cars_in_HH
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3363828
Minimum0
Maximum9
Zeros24468
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:46:07.460636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90902317
Coefficient of variation (CV)0.68021168
Kurtosis3.3440237
Mean1.3363828
Median Absolute Deviation (MAD)1
Skewness0.98460922
Sum214007
Variance0.82632313
MonotonicityNot monotonic
2024-07-05T13:46:07.682048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 74593
46.6%
2 48179
30.1%
0 24468
 
15.3%
3 9853
 
6.2%
4 2255
 
1.4%
5 494
 
0.3%
6 175
 
0.1%
7 60
 
< 0.1%
9 41
 
< 0.1%
8 21
 
< 0.1%
ValueCountFrequency (%)
0 24468
 
15.3%
1 74593
46.6%
2 48179
30.1%
3 9853
 
6.2%
4 2255
 
1.4%
5 494
 
0.3%
6 175
 
0.1%
7 60
 
< 0.1%
8 21
 
< 0.1%
9 41
 
< 0.1%
ValueCountFrequency (%)
9 41
 
< 0.1%
8 21
 
< 0.1%
7 60
 
< 0.1%
6 175
 
0.1%
5 494
 
0.3%
4 2255
 
1.4%
3 9853
 
6.2%
2 48179
30.1%
1 74593
46.6%
0 24468
 
15.3%

Ebike_in_HH
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
126812 
1
33327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160139
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 126812
79.2%
1 33327
 
20.8%

Length

2024-07-05T13:46:07.988126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:08.207648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 126812
79.2%
1 33327
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0 126812
79.2%
1 33327
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160139
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 126812
79.2%
1 33327
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common 160139
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 126812
79.2%
1 33327
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126812
79.2%
1 33327
 
20.8%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9357059
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:46:08.438905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6665389
Coefficient of variation (CV)0.3844654
Kurtosis-0.5699592
Mean6.9357059
Median Absolute Deviation (MAD)2
Skewness-0.67748255
Sum1110677
Variance7.1104299
MonotonicityNot monotonic
2024-07-05T13:46:08.606922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 31935
19.9%
9 26672
16.7%
8 22282
13.9%
7 18635
11.6%
6 15371
9.6%
5 12837
8.0%
4 10834
 
6.8%
1 8271
 
5.2%
3 7956
 
5.0%
2 5346
 
3.3%
ValueCountFrequency (%)
1 8271
 
5.2%
2 5346
 
3.3%
3 7956
 
5.0%
4 10834
 
6.8%
5 12837
8.0%
6 15371
9.6%
7 18635
11.6%
8 22282
13.9%
9 26672
16.7%
10 31935
19.9%
ValueCountFrequency (%)
10 31935
19.9%
9 26672
16.7%
8 22282
13.9%
7 18635
11.6%
6 15371
9.6%
5 12837
8.0%
4 10834
 
6.8%
3 7956
 
5.0%
2 5346
 
3.3%
1 8271
 
5.2%

Trip_distance
Real number (ℝ)

Distinct1351
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.88279
Minimum0
Maximum3716
Zeros59
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:46:08.798160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median30
Q396
95-th percentile500
Maximum3716
Range3716
Interquartile range (IQR)86

Descriptive statistics

Standard deviation219.23426
Coefficient of variation (CV)2.0511652
Kurtosis29.762498
Mean106.88279
Median Absolute Deviation (MAD)23
Skewness4.6514827
Sum17116103
Variance48063.662
MonotonicityNot monotonic
2024-07-05T13:46:09.041648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 14390
 
9.0%
20 11110
 
6.9%
5 8638
 
5.4%
30 8298
 
5.2%
50 6929
 
4.3%
15 5647
 
3.5%
40 4898
 
3.1%
1 3891
 
2.4%
100 3691
 
2.3%
60 3316
 
2.1%
Other values (1341) 89331
55.8%
ValueCountFrequency (%)
0 59
 
< 0.1%
1 3891
2.4%
2 3084
 
1.9%
3 3278
 
2.0%
4 2157
 
1.3%
5 8638
5.4%
6 2133
 
1.3%
7 2035
 
1.3%
8 2618
 
1.6%
9 1311
 
0.8%
ValueCountFrequency (%)
3716 1
< 0.1%
3500 1
< 0.1%
3200 1
< 0.1%
3147 2
< 0.1%
3140 1
< 0.1%
3100 1
< 0.1%
2940 1
< 0.1%
2800 1
< 0.1%
2770 1
< 0.1%
2750 2
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.0
58124 
3.0
54117 
4.0
35025 
2.0
12873 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 58124
36.3%
3.0 54117
33.8%
4.0 35025
21.9%
2.0 12873
 
8.0%

Length

2024-07-05T13:46:09.319607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:09.489640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 58124
36.3%
3.0 54117
33.8%
4.0 35025
21.9%
2.0 12873
 
8.0%

Most occurring characters

ValueCountFrequency (%)
. 160139
33.3%
0 160139
33.3%
1 58124
 
12.1%
3 54117
 
11.3%
4 35025
 
7.3%
2 12873
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160139
50.0%
1 58124
 
18.1%
3 54117
 
16.9%
4 35025
 
10.9%
2 12873
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 160139
33.3%
0 160139
33.3%
1 58124
 
12.1%
3 54117
 
11.3%
4 35025
 
7.3%
2 12873
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 160139
33.3%
0 160139
33.3%
1 58124
 
12.1%
3 54117
 
11.3%
4 35025
 
7.3%
2 12873
 
2.7%

Trip_starthour
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.499166
Minimum0
Maximum26
Zeros101
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:46:09.683593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median14
Q317
95-th percentile21
Maximum26
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3417876
Coefficient of variation (CV)0.32163376
Kurtosis-0.7930044
Mean13.499166
Median Absolute Deviation (MAD)3
Skewness0.042552566
Sum2161743
Variance18.85112
MonotonicityNot monotonic
2024-07-05T13:46:09.890597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 15269
 
9.5%
17 14317
 
8.9%
16 13403
 
8.4%
15 12715
 
7.9%
14 11999
 
7.5%
12 10673
 
6.7%
13 10183
 
6.4%
10 10058
 
6.3%
11 9937
 
6.2%
18 9276
 
5.8%
Other values (17) 42309
26.4%
ValueCountFrequency (%)
0 101
 
0.1%
1 146
 
0.1%
2 137
 
0.1%
3 109
 
0.1%
4 183
 
0.1%
5 637
 
0.4%
6 2741
 
1.7%
7 8593
5.4%
8 15269
9.5%
9 8870
5.5%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 8
 
< 0.1%
24 74
 
< 0.1%
23 2090
 
1.3%
22 2951
 
1.8%
21 3518
 
2.2%
20 4920
 
3.1%
19 7230
4.5%
18 9276
5.8%
17 14317
8.9%

Part_of_sequence
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
143557 
0
16582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160139
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 143557
89.6%
0 16582
 
10.4%

Length

2024-07-05T13:46:10.164622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:10.326917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 143557
89.6%
0 16582
 
10.4%

Most occurring characters

ValueCountFrequency (%)
1 143557
89.6%
0 16582
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160139
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 143557
89.6%
0 16582
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 160139
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 143557
89.6%
0 16582
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 143557
89.6%
0 16582
 
10.4%

PW_no
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
91832 
1.0
68307 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 91832
57.3%
1.0 68307
42.7%

Length

2024-07-05T13:46:10.495868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:10.664854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 91832
57.3%
1.0 68307
42.7%

Most occurring characters

ValueCountFrequency (%)
0 251971
52.4%
. 160139
33.3%
1 68307
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251971
78.7%
1 68307
 
21.3%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251971
52.4%
. 160139
33.3%
1 68307
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251971
52.4%
. 160139
33.3%
1 68307
 
14.2%

PW_yeslessthan12h
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
152853 
1.0
 
7286

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 152853
95.5%
1.0 7286
 
4.5%

Length

2024-07-05T13:46:10.882023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:11.088085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 152853
95.5%
1.0 7286
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 312992
65.2%
. 160139
33.3%
1 7286
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 312992
97.7%
1 7286
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 312992
65.2%
. 160139
33.3%
1 7286
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 312992
65.2%
. 160139
33.3%
1 7286
 
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
136975 
1.0
23164 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 136975
85.5%
1.0 23164
 
14.5%

Length

2024-07-05T13:46:11.266041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:11.437238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 136975
85.5%
1.0 23164
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 297114
61.8%
. 160139
33.3%
1 23164
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 297114
92.8%
1 23164
 
7.2%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 297114
61.8%
. 160139
33.3%
1 23164
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 297114
61.8%
. 160139
33.3%
1 23164
 
4.8%

PW_yesmorethan30h
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
98757 
1.0
61382 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 98757
61.7%
1.0 61382
38.3%

Length

2024-07-05T13:46:11.606344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:11.797799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 98757
61.7%
1.0 61382
38.3%

Most occurring characters

ValueCountFrequency (%)
0 258896
53.9%
. 160139
33.3%
1 61382
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258896
80.8%
1 61382
 
19.2%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 258896
53.9%
. 160139
33.3%
1 61382
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 258896
53.9%
. 160139
33.3%
1 61382
 
12.8%

UO_none
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
86045 
1.0
74094 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 86045
53.7%
1.0 74094
46.3%

Length

2024-07-05T13:46:12.037760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:12.203798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 86045
53.7%
1.0 74094
46.3%

Most occurring characters

ValueCountFrequency (%)
0 246184
51.2%
. 160139
33.3%
1 74094
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 246184
76.9%
1 74094
 
23.1%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 246184
51.2%
. 160139
33.3%
1 74094
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 246184
51.2%
. 160139
33.3%
1 74094
 
15.4%

UO_benefits
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
114505 
1.0
45634 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 114505
71.5%
1.0 45634
 
28.5%

Length

2024-07-05T13:46:12.372056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:12.537034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 114505
71.5%
1.0 45634
 
28.5%

Most occurring characters

ValueCountFrequency (%)
0 274644
57.2%
. 160139
33.3%
1 45634
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274644
85.8%
1 45634
 
14.2%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274644
57.2%
. 160139
33.3%
1 45634
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274644
57.2%
. 160139
33.3%
1 45634
 
9.5%

UO_student/scholar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
119728 
1.0
40411 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 119728
74.8%
1.0 40411
 
25.2%

Length

2024-07-05T13:46:12.724292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:12.924192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 119728
74.8%
1.0 40411
 
25.2%

Most occurring characters

ValueCountFrequency (%)
0 279867
58.3%
. 160139
33.3%
1 40411
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 279867
87.4%
1 40411
 
12.6%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 279867
58.3%
. 160139
33.3%
1 40411
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 279867
58.3%
. 160139
33.3%
1 40411
 
8.4%

HHC_oneperson
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
132650 
1.0
27489 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 132650
82.8%
1.0 27489
 
17.2%

Length

2024-07-05T13:46:13.104999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:13.269731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 132650
82.8%
1.0 27489
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 292789
60.9%
. 160139
33.3%
1 27489
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 292789
91.4%
1 27489
 
8.6%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 292789
60.9%
. 160139
33.3%
1 27489
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 292789
60.9%
. 160139
33.3%
1 27489
 
5.7%

HHC_couple
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
115505 
1.0
44634 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 115505
72.1%
1.0 44634
 
27.9%

Length

2024-07-05T13:46:13.448997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:13.603306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 115505
72.1%
1.0 44634
 
27.9%

Most occurring characters

ValueCountFrequency (%)
0 275644
57.4%
. 160139
33.3%
1 44634
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 275644
86.1%
1 44634
 
13.9%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 275644
57.4%
. 160139
33.3%
1 44634
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 275644
57.4%
. 160139
33.3%
1 44634
 
9.3%

HHC_couple_with_children
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
83306 
1.0
76833 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 83306
52.0%
1.0 76833
48.0%

Length

2024-07-05T13:46:13.788714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:13.970420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 83306
52.0%
1.0 76833
48.0%

Most occurring characters

ValueCountFrequency (%)
0 243445
50.7%
. 160139
33.3%
1 76833
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 243445
76.0%
1 76833
 
24.0%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 243445
50.7%
. 160139
33.3%
1 76833
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 243445
50.7%
. 160139
33.3%
1 76833
 
16.0%

HHC_oneperson_with_children
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
148956 
1.0
 
11183

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 148956
93.0%
1.0 11183
 
7.0%

Length

2024-07-05T13:46:14.171552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:14.338623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 148956
93.0%
1.0 11183
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 309095
64.3%
. 160139
33.3%
1 11183
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 309095
96.5%
1 11183
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 309095
64.3%
. 160139
33.3%
1 11183
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 309095
64.3%
. 160139
33.3%
1 11183
 
2.3%

Ethn_dutch
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.0
130691 
0.0
29448 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 130691
81.6%
0.0 29448
 
18.4%

Length

2024-07-05T13:46:14.505485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:14.655722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 130691
81.6%
0.0 29448
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 189587
39.5%
. 160139
33.3%
1 130691
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 189587
59.2%
1 130691
40.8%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 189587
39.5%
. 160139
33.3%
1 130691
27.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 189587
39.5%
. 160139
33.3%
1 130691
27.2%

Ethn_western
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
146387 
1.0
 
13752

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 146387
91.4%
1.0 13752
 
8.6%

Length

2024-07-05T13:46:14.838959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:15.005070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 146387
91.4%
1.0 13752
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 306526
63.8%
. 160139
33.3%
1 13752
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 306526
95.7%
1 13752
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 306526
63.8%
. 160139
33.3%
1 13752
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 306526
63.8%
. 160139
33.3%
1 13752
 
2.9%

Ethn_nonwestern
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
144443 
1.0
15696 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 144443
90.2%
1.0 15696
 
9.8%

Length

2024-07-05T13:46:15.189422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:15.374890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 144443
90.2%
1.0 15696
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 304582
63.4%
. 160139
33.3%
1 15696
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 304582
95.1%
1 15696
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 304582
63.4%
. 160139
33.3%
1 15696
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 304582
63.4%
. 160139
33.3%
1 15696
 
3.3%

EL_primary
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
132819 
1.0
27320 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 132819
82.9%
1.0 27320
 
17.1%

Length

2024-07-05T13:46:15.541193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:15.704790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 132819
82.9%
1.0 27320
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 292958
61.0%
. 160139
33.3%
1 27320
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 292958
91.5%
1 27320
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 292958
61.0%
. 160139
33.3%
1 27320
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 292958
61.0%
. 160139
33.3%
1 27320
 
5.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
139861 
1.0
20278 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 139861
87.3%
1.0 20278
 
12.7%

Length

2024-07-05T13:46:15.888450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:16.057434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 139861
87.3%
1.0 20278
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0 300000
62.4%
. 160139
33.3%
1 20278
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 300000
93.7%
1 20278
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 300000
62.4%
. 160139
33.3%
1 20278
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 300000
62.4%
. 160139
33.3%
1 20278
 
4.2%

EL_secondary_higher
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
112254 
1.0
47885 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 112254
70.1%
1.0 47885
29.9%

Length

2024-07-05T13:46:16.230324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:16.404404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112254
70.1%
1.0 47885
29.9%

Most occurring characters

ValueCountFrequency (%)
0 272393
56.7%
. 160139
33.3%
1 47885
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 272393
85.0%
1 47885
 
15.0%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 272393
56.7%
. 160139
33.3%
1 47885
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 272393
56.7%
. 160139
33.3%
1 47885
 
10.0%

EL_higherprofessional_university
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
95483 
1.0
64656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 95483
59.6%
1.0 64656
40.4%

Length

2024-07-05T13:46:16.604357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:16.773614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 95483
59.6%
1.0 64656
40.4%

Most occurring characters

ValueCountFrequency (%)
0 255622
53.2%
. 160139
33.3%
1 64656
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255622
79.8%
1 64656
 
20.2%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255622
53.2%
. 160139
33.3%
1 64656
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255622
53.2%
. 160139
33.3%
1 64656
 
13.5%

Moti_work
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
122515 
1.0
37624 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 122515
76.5%
1.0 37624
 
23.5%

Length

2024-07-05T13:46:16.958075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:17.138092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 122515
76.5%
1.0 37624
 
23.5%

Most occurring characters

ValueCountFrequency (%)
0 282654
58.8%
. 160139
33.3%
1 37624
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 282654
88.3%
1 37624
 
11.7%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 282654
58.8%
. 160139
33.3%
1 37624
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 282654
58.8%
. 160139
33.3%
1 37624
 
7.8%

Moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
158640 
1.0
 
1499

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 158640
99.1%
1.0 1499
 
0.9%

Length

2024-07-05T13:46:17.319237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:17.505216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 158640
99.1%
1.0 1499
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 318779
66.4%
. 160139
33.3%
1 1499
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 318779
99.5%
1 1499
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 318779
66.4%
. 160139
33.3%
1 1499
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 318779
66.4%
. 160139
33.3%
1 1499
 
0.3%

Moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
149411 
1.0
 
10728

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 149411
93.3%
1.0 10728
 
6.7%

Length

2024-07-05T13:46:17.689926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:17.851408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 149411
93.3%
1.0 10728
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 309550
64.4%
. 160139
33.3%
1 10728
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 309550
96.7%
1 10728
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 309550
64.4%
. 160139
33.3%
1 10728
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 309550
64.4%
. 160139
33.3%
1 10728
 
2.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
124621 
1.0
35518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 124621
77.8%
1.0 35518
 
22.2%

Length

2024-07-05T13:46:18.034692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:18.188457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 124621
77.8%
1.0 35518
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 284760
59.3%
. 160139
33.3%
1 35518
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 284760
88.9%
1 35518
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 284760
59.3%
. 160139
33.3%
1 35518
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 284760
59.3%
. 160139
33.3%
1 35518
 
7.4%

Moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
85369 
1.0
74770 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 85369
53.3%
1.0 74770
46.7%

Length

2024-07-05T13:46:18.359464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:18.528620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85369
53.3%
1.0 74770
46.7%

Most occurring characters

ValueCountFrequency (%)
0 245508
51.1%
. 160139
33.3%
1 74770
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 245508
76.7%
1 74770
 
23.3%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 245508
51.1%
. 160139
33.3%
1 74770
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 245508
51.1%
. 160139
33.3%
1 74770
 
15.6%

Main_moti_work
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
112891 
1.0
47248 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 112891
70.5%
1.0 47248
29.5%

Length

2024-07-05T13:46:18.736034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:18.909089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 112891
70.5%
1.0 47248
29.5%

Most occurring characters

ValueCountFrequency (%)
0 273030
56.8%
. 160139
33.3%
1 47248
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 273030
85.2%
1 47248
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 273030
56.8%
. 160139
33.3%
1 47248
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 273030
56.8%
. 160139
33.3%
1 47248
 
9.8%

Main_moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157296 
1.0
 
2843

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157296
98.2%
1.0 2843
 
1.8%

Length

2024-07-05T13:46:19.096031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:19.267645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157296
98.2%
1.0 2843
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 317435
66.1%
. 160139
33.3%
1 2843
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 317435
99.1%
1 2843
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 317435
66.1%
. 160139
33.3%
1 2843
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 317435
66.1%
. 160139
33.3%
1 2843
 
0.6%

Main_moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
153369 
1.0
 
6770

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 153369
95.8%
1.0 6770
 
4.2%

Length

2024-07-05T13:46:19.445907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:19.614561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 153369
95.8%
1.0 6770
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 313508
65.3%
. 160139
33.3%
1 6770
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 313508
97.9%
1 6770
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 313508
65.3%
. 160139
33.3%
1 6770
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 313508
65.3%
. 160139
33.3%
1 6770
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
126025 
1.0
34114 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 126025
78.7%
1.0 34114
 
21.3%

Length

2024-07-05T13:46:19.785360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:19.989010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 126025
78.7%
1.0 34114
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 286164
59.6%
. 160139
33.3%
1 34114
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 286164
89.3%
1 34114
 
10.7%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 286164
59.6%
. 160139
33.3%
1 34114
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 286164
59.6%
. 160139
33.3%
1 34114
 
7.1%

Main_moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
90975 
1.0
69164 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters480417
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 90975
56.8%
1.0 69164
43.2%

Length

2024-07-05T13:46:20.179071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:46:20.350716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 90975
56.8%
1.0 69164
43.2%

Most occurring characters

ValueCountFrequency (%)
0 251114
52.3%
. 160139
33.3%
1 69164
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320278
66.7%
Other Punctuation 160139
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251114
78.4%
1 69164
 
21.6%
Other Punctuation
ValueCountFrequency (%)
. 160139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 480417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251114
52.3%
. 160139
33.3%
1 69164
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251114
52.3%
. 160139
33.3%
1 69164
 
14.4%

Interactions

2024-07-05T13:45:58.195071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:50.855180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:52.652458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:53.971632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:55.223539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:56.740875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:58.386759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:51.155917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:52.888228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:54.171476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:55.608921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:56.988766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:58.675614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:51.389044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:53.121917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:54.390189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:55.894815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:57.186937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:59.021797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:51.647933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:53.321876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:54.588672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:56.104078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:57.441828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:59.219719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:51.938097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:53.521712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:54.787898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:56.305517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:57.679484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:59.436451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:52.199576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:53.754752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:55.016254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:56.520863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:45:57.905146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T13:46:20.550821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeDisposable_income_householdEL_higherprofessional_universityEL_primaryEL_secondary_higherEL_secondary_lowerEbike_in_HHEthn_dutchEthn_nonwesternEthn_westernGender_maleHHC_coupleHHC_couple_with_childrenHHC_onepersonHHC_oneperson_with_childrenMain_moti_pickupdropoff_goodsMain_moti_pickupdropoff_personMain_moti_professionMain_moti_sparetimeMain_moti_workMoti_pickupdropoff_goodsMoti_pickupdropoff_personMoti_professionMoti_sparetimeMoti_workNumber_of_cars_in_HHPW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hPart_of_sequenceStarting_postalcodeTrip_distanceTrip_starthourTrip_transportation_typeUO_benefitsUO_noneUO_student/scholar
Age1.000-0.1200.4080.8000.2980.2830.2660.1060.1570.0410.0790.5220.5120.2260.1720.0500.0570.0220.1010.0800.1860.2070.0410.3590.280-0.0370.6870.2250.2390.5590.0840.0270.141-0.0270.2440.6600.6550.890
Disposable_income_household-0.1201.0000.1690.0700.0830.1840.0860.1130.1090.0450.0490.1530.4390.4880.1810.0180.0140.0170.0140.0180.0900.0570.0120.0220.0530.4380.1780.0510.0710.1580.034-0.0540.0450.0030.0750.2670.1720.173
EL_higherprofessional_university0.4080.1691.0000.3730.5370.3130.0740.0210.0590.0340.0230.0860.0990.0860.0840.0060.0110.0180.0540.0540.0000.0550.0110.1450.141-0.0290.3150.0730.0350.3260.007-0.0760.0870.0450.1150.0550.3390.332
EL_primary0.8000.0700.3731.0000.2960.1730.0380.0820.1100.0030.0110.1910.2260.1350.0930.0190.0190.0070.0620.0430.0800.0760.0230.2640.1820.0240.4080.0000.1510.3050.0150.001-0.185-0.0380.2990.1410.3580.557
EL_secondary_higher0.2980.0830.5370.2961.0000.2490.0190.0170.0090.0140.0050.0260.0000.0180.0220.0000.0000.0090.0060.0090.0200.0210.0260.0550.0260.0310.0850.0500.1000.0060.0050.0420.0530.0080.1110.0060.0230.034
EL_secondary_lower0.2830.1840.3130.1730.2491.0000.1270.0380.0240.0260.0140.1240.1060.0000.0120.0140.0050.0050.0000.0180.0620.0240.0060.0080.038-0.0270.1220.0400.0190.1270.0000.0530.008-0.0350.0210.2300.1260.094
Ebike_in_HH0.2660.0860.0740.0380.0190.1271.0000.1160.1120.0420.0050.1500.0290.1010.0590.0180.0000.0070.0040.0230.0290.0120.0000.0160.0400.0830.0980.0070.0040.0990.0250.0840.047-0.0150.0720.1710.0910.073
Ethn_dutch0.1060.1130.0210.0820.0170.0380.1161.0000.6940.6460.0280.0630.0060.0270.0840.0110.0000.0000.0070.0000.0040.0060.0130.0000.0020.1530.0360.0000.0230.0210.0410.1680.0460.0040.1300.0300.0320.068
Ethn_nonwestern0.1570.1090.0590.1100.0090.0240.1120.6941.0000.1010.0200.0990.0310.0000.1100.0130.0020.0060.0150.0040.0050.0010.0070.0130.009-0.1290.0430.0130.0200.0350.044-0.153-0.046-0.0030.1380.0580.0510.118
Ethn_western0.0410.0450.0340.0030.0140.0260.0420.6460.1011.0000.0180.0180.0430.0350.0000.0000.0000.0020.0050.0070.0120.0050.0100.0160.006-0.0740.0040.0120.0110.0080.010-0.070-0.015-0.0020.0330.0190.0090.031
Gender_male0.0790.0490.0230.0110.0050.0140.0050.0280.0200.0181.0000.0620.0110.0110.0700.0000.0120.0140.0070.0090.0410.0610.0360.0000.0670.0250.0260.0750.2680.2520.000-0.0020.1210.0100.0980.0470.0720.034
HHC_couple0.5220.1530.0860.1910.0260.1240.1500.0630.0990.0180.0621.0000.5970.2830.1700.0220.0100.0090.0200.0030.0840.0750.0020.0560.027-0.0480.0470.0540.0580.0160.0000.0240.081-0.0000.0910.2890.0000.299
HHC_couple_with_children0.5120.4390.0990.2260.0000.1060.0290.0060.0310.0430.0110.5971.0000.4370.2630.0220.0220.0030.0150.0070.1110.1280.0040.0550.0320.4140.0570.0440.0830.0210.0220.026-0.049-0.0360.0790.2780.0250.260
HHC_oneperson0.2260.4880.0860.1350.0180.0000.1010.0270.0000.0350.0110.2830.4371.0000.1250.0050.0210.0140.0070.0160.0520.0820.0030.0310.035-0.4090.0050.0170.0580.0560.022-0.042-0.0080.0460.0810.0730.0060.083
HHC_oneperson_with_children0.1720.1810.0840.0930.0220.0120.0590.0840.1100.0000.0700.1700.2630.1251.0000.0040.0070.0020.0180.0170.0060.0000.0080.0380.037-0.1220.0370.0350.0240.0700.010-0.032-0.0340.0030.0530.0720.0560.140
Main_moti_pickupdropoff_goods0.0500.0180.0060.0190.0000.0140.0180.0110.0130.0000.0000.0220.0220.0050.0041.0000.1090.0700.4540.3370.1200.0160.0060.0440.0540.0020.0140.0090.0080.0170.0000.011-0.010-0.0240.0330.0440.0090.035
Main_moti_pickupdropoff_person0.0570.0140.0110.0190.0000.0050.0000.0000.0020.0000.0120.0100.0220.0210.0070.1091.0000.0280.1830.1360.0130.1260.0000.0330.0220.0070.0150.0000.0240.0000.006-0.006-0.001-0.0160.0250.0200.0100.033
Main_moti_profession0.0220.0170.0180.0070.0090.0050.0070.0000.0060.0020.0140.0090.0030.0140.0020.0700.0281.0000.1170.0870.0020.0030.0970.0050.0100.0140.0090.0000.0050.0030.0050.0170.009-0.0050.0170.0080.0000.008
Main_moti_sparetime0.1010.0140.0540.0620.0060.0000.0040.0070.0150.0050.0070.0200.0150.0070.0180.4540.1830.1171.0000.5640.0500.0260.0130.1270.0820.0080.0830.0160.0280.0710.0000.013-0.012-0.0020.0310.0030.0830.100
Main_moti_work0.0800.0180.0540.0430.0090.0180.0230.0000.0040.0070.0090.0030.0070.0160.0170.3370.1360.0870.5641.0000.0460.0110.0060.0820.150-0.0170.0940.0100.0110.0910.003-0.0260.0200.0320.0250.0470.0940.060
Moti_pickupdropoff_goods0.1860.0900.0000.0800.0200.0620.0290.0040.0050.0120.0410.0840.1110.0520.0060.1200.0130.0020.0500.0461.0000.1430.0520.5000.296-0.0650.0640.0110.0040.0630.0730.020-0.1840.0090.0780.1790.0510.128
Moti_pickupdropoff_person0.2070.0570.0550.0760.0210.0240.0120.0060.0010.0050.0610.0750.1280.0820.0000.0160.1260.0030.0260.0110.1431.0000.0260.2510.1480.0530.0590.0020.0920.0040.0440.007-0.040-0.0300.1300.0530.0490.112
Moti_profession0.0410.0120.0110.0230.0260.0060.0000.0130.0070.0100.0360.0020.0040.0030.0080.0060.0000.0970.0130.0060.0520.0261.0000.0910.0540.0270.0540.0100.0140.0410.0100.0080.0460.0080.0560.0210.0480.032
Moti_sparetime0.3590.0220.1450.2640.0550.0080.0160.0000.0130.0160.0000.0560.0550.0310.0380.0440.0330.0050.1270.0820.5000.2510.0911.0000.5190.0060.2790.0290.0950.2280.0040.012-0.0350.1400.2230.0260.2670.333
Moti_work0.2800.0530.1410.1820.0260.0380.0400.0020.0090.0060.0670.0270.0320.0350.0370.0540.0220.0100.0820.1500.2960.1480.0540.5191.0000.0190.3440.0240.0500.3240.094-0.0390.234-0.1580.1770.1720.3240.193
Number_of_cars_in_HH-0.0370.438-0.0290.0240.031-0.0270.0830.153-0.129-0.0740.025-0.0480.414-0.409-0.1220.0020.0070.0140.008-0.017-0.0650.0530.0270.0060.0191.0000.1210.0340.0680.0730.0680.1140.136-0.0150.1840.1550.1060.100
PW_no0.6870.1780.3150.4080.0850.1220.0980.0360.0430.0040.0260.0470.0570.0050.0370.0140.0150.0090.0830.0940.0640.0590.0540.2790.3440.1211.0000.1880.3550.6800.0060.035-0.169-0.0390.2470.4570.8000.444
PW_yeslessthan12h0.2250.0510.0730.0000.0500.0400.0070.0000.0130.0120.0750.0540.0440.0170.0350.0090.0000.0000.0160.0100.0110.0020.0100.0290.0240.0340.1881.0000.0900.1720.0170.005-0.0310.0090.0760.0230.1220.164
PW_yesmorethan12to30h0.2390.0710.0350.1510.1000.0190.0040.0230.0200.0110.2680.0580.0830.0580.0240.0080.0240.0050.0280.0110.0040.0920.0140.0950.0500.0680.3550.0901.0000.3240.0240.026-0.013-0.0050.0570.0910.1960.131
PW_yesmorethan30h0.5590.1580.3260.3050.0060.1270.0990.0210.0350.0080.2520.0160.0210.0560.0700.0170.0000.0030.0710.0910.0630.0040.0410.2280.3240.0730.6800.1720.3241.0000.003-0.0570.1940.0400.2410.3890.7250.428
Part_of_sequence0.0840.0340.0070.0150.0050.0000.0250.0410.0440.0100.0000.0000.0220.0220.0100.0000.0060.0050.0000.0030.0730.0440.0100.0040.0940.0680.0060.0170.0240.0031.0000.031-0.0290.3180.2430.0320.0110.046
Starting_postalcode0.027-0.054-0.0760.0010.0420.0530.0840.168-0.153-0.070-0.0020.0240.026-0.042-0.0320.011-0.0060.0170.013-0.0260.0200.0070.0080.012-0.0390.1140.0350.0050.026-0.0570.0311.0000.043-0.0240.1040.0510.0420.027
Trip_distance0.1410.0450.087-0.1850.0530.0080.0470.046-0.046-0.0150.1210.081-0.049-0.008-0.034-0.010-0.0010.009-0.0120.020-0.184-0.0400.046-0.0350.2340.136-0.169-0.031-0.0130.194-0.0290.0431.000-0.0400.1750.0480.1150.085
Trip_starthour-0.0270.0030.045-0.0380.008-0.035-0.0150.004-0.003-0.0020.010-0.000-0.0360.0460.003-0.024-0.016-0.005-0.0020.0320.009-0.0300.0080.140-0.158-0.015-0.0390.009-0.0050.0400.318-0.024-0.0401.0000.0740.1810.1880.076
Trip_transportation_type0.2440.0750.1150.2990.1110.0210.0720.1300.1380.0330.0980.0910.0790.0810.0530.0330.0250.0170.0310.0250.0780.1300.0560.2230.1770.1840.2470.0760.0570.2410.2430.1040.1750.0741.0000.1050.2550.362
UO_benefits0.6600.2670.0550.1410.0060.2300.1710.0300.0580.0190.0470.2890.2780.0730.0720.0440.0200.0080.0030.0470.1790.0530.0210.0260.1720.1550.4570.0230.0910.3890.0320.0510.0480.1810.1051.0000.5860.367
UO_none0.6550.1720.3390.3580.0230.1260.0910.0320.0510.0090.0720.0000.0250.0060.0560.0090.0100.0000.0830.0940.0510.0490.0480.2670.3240.1060.8000.1220.1960.7250.0110.0420.1150.1880.2550.5861.0000.539
UO_student/scholar0.8900.1730.3320.5570.0340.0940.0730.0680.1180.0310.0340.2990.2600.0830.1400.0350.0330.0080.1000.0600.1280.1120.0320.3330.1930.1000.4440.1640.1310.4280.0460.0270.0850.0760.3620.3670.5391.000

Missing values

2024-07-05T13:45:59.871618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T13:46:01.549334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
09901.014210860.01.010.000.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.0
19933.014210860.01.011.010.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.0
29901.014210810.03.015.010.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.0
39902.014210810.03.018.010.00.00.01.01.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.0
49902.014210330.01.09.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.0
59902.014210330.01.09.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.0
69902.014210310.04.013.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.0
79785.0160101080.03.07.010.00.00.01.01.00.00.00.01.00.00.01.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.0
89743.016010102.04.012.010.00.00.01.01.00.00.00.01.00.00.01.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.0
99743.016010102.04.012.010.00.00.01.01.00.00.00.01.00.00.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.01.00.0
Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
1601293015.015310225.03.02.010.00.01.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.01.00.0
1601303032.015310220.03.012.010.00.01.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.0
1601313086.0146101070.01.014.000.00.00.01.01.00.00.00.00.01.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.01.00.0
160132NaN146101070.01.014.010.00.00.01.01.00.00.00.00.01.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.01.00.00.0
1601332904.006810860.01.010.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.01.00.00.0
1601342923.006810860.01.012.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.01.00.00.0
1601352904.006810877.02.018.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.01.00.00.0
1601362904.00681083.04.018.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.01.00.00.0
1601373012.00681083.04.021.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.01.00.00.0
1601383012.006810870.02.021.011.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.01.00.00.0

Duplicate rows

Most frequently occurring

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime# duplicates
26855505.003310101.01.016.000.00.01.00.01.00.00.00.00.00.01.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.01.00.010
4321381.0091041.04.014.011.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.01.00.00.00.00.01.00.08
11072542.00371035.04.08.001.00.00.00.00.01.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.01.00.08
39468232.00661051.04.011.001.00.00.00.00.01.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.00.01.00.00.00.00.08
10312401.00252071.04.012.000.00.00.01.00.00.01.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.01.00.00.00.00.07
4361382.0091041.04.014.011.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.01.00.00.00.00.01.00.06
6061648.00420093.03.08.000.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.01.00.06
16233317.00401062.01.08.001.00.00.00.00.01.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.00.06
34097031.00632091.03.010.000.01.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.01.06
9002202.00731013.04.07.001.00.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.01.00.00.00.00.05